ArticleFrontiers in neuroinformatics2020
A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets.
Article in Frontiers in neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 19 citations in OpenAlex.
- Modeling 3D mesoscaled neuronal complexity through learning-based dynamic morphometric convolution.Brain informatics · 2026Article
- Super-resolution microscopy and deep learning methods: what can they bring to neuroscience: from neuron to 3D spine segmentation.Frontiers in neuroinformatics · 2025Review
- A modular framework for multi-scale tissue imaging and neuronal segmentation.Nature communications · 2024Article
- Article
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Classification of Geometric Forms in Mosaics Using Deep Neural Network.Journal of imaging · 2021Article
- Article
- TISSUE CLEARING.Nature reviews. Methods primers · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Accurately digitizing the brain at the micro-scale is crucial for investigating brain structure-function relationships and documenting morphological alterations due to neuropathies. Here we present a new Smart Region Growing algorithm (SmRG) for the segmentation of single neurons in their intricate 3D arrangement within the brain. Its Region Growing procedure is based on a homogeneity predicate determined by describing the pixel intensity statistics of confocal acquisitions with a mixture model, enabling an accurate reconstruction of complex 3D cellular structures from high-resolution images of neural tissue. The algorithm's outcome is a 3D matrix of logical values identifying the voxels belonging to the segmented structure, thus providing additional useful volumetric information on neurons. To highlight the algorithm's full potential, we compared its performance in terms of accuracy, reproducibility, precision and robustness of 3D neuron reconstructions based on microscopic data from different brain locations and imaging protocols against both manual and state-of-the-art reconstruction tools.
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